Predictive control of zero moment point (ZMP) for terrain robot kinematics

Abstract

Maintaining stability and completing the required trajectory are the most important motion control issues for a six-kinematic-degrees-of-freedom legged robot. Zero-moment point (ZMP) approaches have evolved into an indispensable tool for obtaining dynamic equilibrium in humanoid or bipod robots for walking and handling. It's point where momentary, applied, and reactive forces cancel out. Despite ZMP advancements, legged robots continue to struggle with a number of essential skills exhibited by primates, such as like the Atlas robot, which can walk but not jump, run, or sprint. In complex environments, it may be incapable of interception. Hence, in this study, the effect of center of mass (CoM) trajectory on biped robot stability has been explored to understand the secure walking behavior. The research investigates the predictive control technique of adjusting CoM trajectory to generate stable walking patterns, which pioneered the concept of altering the CoM in advance during human walking and reacting to changing path restrictions. This section explains how to fix continuous variation's ZMP tracking problem. This allows monitoring the necessary ZMP, expanding gait generation systems and their analysis. Predictive control can produce secure walking models by manipulating the COM and CoG pathline in bipod robots.